A study of Liu-Storey conjugate gradient methods for vector optimization

M.L.N. Gonçalves, F.S. Lima and L.F. Prudente

Applied Mathematics and Computation, 2022, vol. 425, issue C

Abstract: This work presents a study of Liu-Storey (LS) nonlinear conjugate gradient (CG) methods to solve vector optimization problems. Three variants of the LS-CG method originally designed to solve single-objective problems are extended to the vector setting. The first algorithm restricts the LS conjugate parameter to be nonnegative and use a sufficiently accurate line search satisfying the (vector) standard Wolfe conditions. The second algorithm combines a modification in the LS conjugate parameter with a line search satisfying the (vector) strong Wolfe conditions. The third algorithm consists of a combination of the LS conjugate parameter with a new Armijo-type line search (to be proposed here for the vector setting). Global convergence results and numerical experiments are presented.

Keywords: Vector optimization; Conjugate gradient methods; Global convergence; Pareto efficiency (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:apmaco:v:425:y:2022:i:c:s0096300322001837

DOI: 10.1016/j.amc.2022.127099

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